Cellular communications networks enable wireless communication for various types of wireless devices. Positioning of wireless devices within a cellular communications network has many emerging applications ranging from security and emergency services to environmental monitoring to command and control. While technologies such as the Global Positioning System (GPS) have enabled accurate positioning of wireless devices in most environments, there are still many challenging environments in which positioning of wireless devices is difficult (e.g., inside buildings, inside tunnels, etc.). In these challenging environments, positioning of a wireless device can be determined by estimating different parameters of a radio signal received by the wireless device such as a Time of Arrival (TOA) of the radio signal, a Direction of Arrival (DOA) of the radio signal, and a Received Signal Strength (RSS) of the radio signal. The accuracy of TOA and DOA-based positioning techniques is greater than that of other techniques such as RSS-based positioning techniques.
One issue with TOA and DOA-based positioning techniques is multi-path propagation, or scattering, of the received radio signal. As a result of multi-path propagation, multiple versions of the radio signal arrive at the receiver via multiple different radio propagation paths. Each of these radio propagation paths has different characteristics (e.g., different time delays). Utilization of TOA and DOA-based positioning techniques in a multi-path environment requires a Line of Sight (LOS) path between the transmitter and the receiver.
Several mechanisms exist for detecting LOS in robotics and direct point-to-point wireless systems. In particular, a paper entitled “Incremental Multi-Robot Deployment for Line of Sight Chains Using Only Radio Signal Strength” by John O'Hollaren and Dylan Shell presented at the International Conference on Robotics and Automation (ICRA) 2010 Multi Robot Autonomy Workshop describes a technique for determining whether two robots that are equipped with wireless radios are within LOS of one another using signal statistics from the wireless radios. In particular, the paper teaches that a Root Mean Square Error (RMSE) between a Rayleigh Probability Density Function (PDF) and Received Signal Strength Indicator (RSSI) measurements for the received radio signal at the wireless device of one of the robots is a good indicator of LOS or non-LOS. In particular, a low RMSE is a good indicator of LOS, whereas a large RMSE is a good indicator of non-LOS.
Further, U.S. Patent Application Publication No. 2012/0225665, entitled CHARACTERIZATION OF A WIRELESS COMMUNICATIONS LINK, teaches making a LOS determination based on known locations of a transmitter and a receiver, a known transmit power of the transmitter, and a known received power at the receiver. Using this information, an observed path loss is calculated and compared to a modeled path loss. If a difference between the observed path loss and the modeled path loss is small, then a determination is made that there is a LOS path between the transmitter and the receiver. A bypass mode is also taught wherein, if a distance between the transmitter and the receiver is small, the path between the transmitter and the receiver is presumed to be a LOS path.
However, prior mechanisms for detecting LOS, such as the ones discussed above, are not reliable in all situations. For example, the technique taught in the O'Hollaren paper is not reliable under a low mobility scenario unless a large number of RSSI measurements are used. A large number of RSSI measurements leads to complex processing at the receiver, which is not desirable in many applications. The technique taught by U.S. Patent Application Publication No. 2012/0225665 requires a known location for both the transmitter and the receiver and, as such, is not reliable in scenarios where the location of the transmitter and/or the location of the receiver are not known (e.g., an application where LOS detection is part of a positioning process to determine a position, or location, of the transmitter or the receiver). Further, as a result of the issues noted above, prior mechanisms cannot be used to reliably detect LOS in all scenarios that are often encountered in complex environments such as a modern cellular communications network, which have various mobility, distance, and propagation scenarios. As such, there is a need for systems and methods for reliably detecting LOS in a cellular communications network.